{"id":9854,"date":"2026-08-12T14:33:50","date_gmt":"2026-08-12T06:33:50","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9854"},"modified":"2026-08-17T23:50:48","modified_gmt":"2026-08-17T15:50:48","slug":"jase-202611-34-042","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-042","title":{"rendered":"Enhancing Coal-Fired Power Plant Efficiency through Edge Computing and Video Processing for Real-Time Emission Monitoring"},"content":{"rendered":"\n
\n
\n

<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

\n

<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n

Lingyun Yang1<\/sup><\/i><\/a>, Minglong Wang1<\/sup>, Qianchuan Zhao2<\/sup>, Wenfeng Yang1<\/sup>, and Yang Li1<\/sup><\/p>\n\n\n\n

1<\/sup>GUIZHOU CASICloud-tech Co.,Ltd, Guiyang, Guizhou,550001, China<\/p>\n\n\n\n

2<\/sup>Department of Automation, Tsinghua University,100084, China<\/p>\n<\/div>\n\n\n\n

\n

Received: April 14, 2026
Accepted: May 16, 2026
Publication Date: August 12, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Real-time video emission monitoring based onedge-cloud collaboration<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.042<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

<\/div>\n\n\n\n

To address limitations in real-time responsiveness, spatial perception, and long-term stability in emission monitoring of coal-fired power plants, this paper proposes an edge\u2013cloud collaborative video-based monitoring framework. The system integrates video acquisition, image preprocessing, multi-scale target detection, emission state recognition, and cloud-based collaborative analysis into a unified pipeline. An improved YOLO-FPN model is developed to enhance the detection of plume regions, flame instances, and abnormal operating zones. In addition, a multi-node edge collaboration strategy with dynamic model updating is introduced to reduce latency and improve adaptive performance over time. Experiments conducted on a real industrial testbed demonstrate improved detection accuracy, reduced processing delay, and lower network bandwidth consumption, while maintaining stable online deployment capability.<\/p>\n\n\n\n

Keywords: Coal-fired power plants; edge-cloud collaboration; video monitoring; YOLO-FPN; edge computing; real-time emission monitoring; target detection<\/em><\/p>\n\n\n\n

<\/div>\n\n\n\n
\n
\n
\n
    \n
  1. [1] H. Xie and Y. Liu, (2025) \u201cThe Application of Digital Technologies in Carbon Emission Reduction in China\u2019s Energy and Power Industry “Net Zero 1(1):6\u201319.URL: https:\/\/www.coscipress.com\/journal\/NZ\/article\/4cbd382f03974996c09ae896c39cf61f.<\/li>\n
  2. [2] M. A. Miah et al., (2025) \u201cBig Data Analytics for Enhancing Coal-Based Energy Production Amidst AI Infrastructure Growth\u201d Journal of Posthumanism 5(5): 5061\u20135080. URL: https:\/\/www.ceeol.com\/search\/article-detail?id=1360747<\/a>.<\/li>\n
  3. [3] J. Xia et al., (2024) \u201cLAS-on-edge: A Real-Time Laser Absorption Spectroscopic Water Vapor Sensor on Edge Computing Platforms\u201d Sensors and Actuators B: Chemical 418: 136258. DOI: 10.1016\/j.snb.2024.136258.<\/li>\n
  4. [4] F. Wang et al., (2025) \u201cResearching the Landscape of Predictive Emissions Monitoring System: A Review of Literature and Technology Trends\u201d Environmental Systems Research 14(1): 11. DOI: 10.1186\/s40068-025-00403-9.<\/li>\n
  5. [5] A. Yavari et al., (2024) \u201cHydrogen 4.0: A Cyber-Physical System for Renewable Hydrogen Energy Plants\u201d Sensors 24(10): 3239. DOI: 10.3390\/s24103239.<\/li>\n
  6. [6] X. Zhai, Y. Peng, and X. Guo, (2024) \u201cEdge-Cloud Collaboration for Low-Latency, Low-Carbon, and Cost-Efficient Operations\u201d Computers and Electrical Engineering 120: 109758. DOI: 10.1016\/j.compeleceng.2024.109758.<\/li>\n
  7. [7] M. Maksimovi\u0107, S. Joki\u0107, and M. \u010c. Bo\u0161kovi\u0107, (2025) \u201cInnovative Horizons for Sustainable Smart Energy: Exploring the Synergy of 5G and Digital Twin Technologies\u201d Process Integration and Optimization for Sustainability 9(2): 431\u2013470. DOI: 10.1007\/s41660-024-00478-4.<\/li>\n
  8. [8] S. Manikandan et al., (2025) \u201cArtificial Intelligence-Driven Sustainability: Enhancing Carbon Capture for Sustainable Development Goals\u2013A Review\u201d Sustainable Development 33(2): 2004\u20132029. DOI: 10.1002\/sd.3222.<\/li>\n
  9. [9] H. Wang et al. \u201cISMF-Net: An Integrated Multi-Modal, Multi-Task Framework for Intelligent Monitoring of Wet Slag Removal Systems\u201d. In: 2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2025, 1\u20138. DOI: 10.1109\/ICPADS67057.2025.11322916.<\/li>\n
  10. [10] M. R. Kabir, D. Halder, and S. Ray, (2024) \u201cDigital Twins for IoT-Driven Energy Systems: A Survey\u201d IEEE Access 12: 177123\u2013177143. DOI: 10.1109\/ACCESS.2024.3506660.<\/li>\n
  11. [11] G. Xiao, Y. Lin, and C. Xu, (2025) \u201cAn On-Line Measurement Technique for Slag Flowrate in Dry Slag Discharge Machine Based on Active Binocular Stereovision\u201d Measurement 249: 117016. DOI: 10.1016\/j.measurement.2025.117016.<\/li>\n
  12. [12] Y. Dou, X. Zhang, and L. Chen, (2024) \u201cResearch on Optimal Carbon Emissions in the Production Decision of the Coal-Fired Power Plant\u201d International Journal of Energy Sector Management 18(6): 1630\u20131648. DOI: 10.1108\/IJESM-07-2023-0019.<\/li>\n
  13. [13] Y. Zhang and G. Wu, (2025) \u201cYOLOv8n-BWG-Enhanced Drone Smoke Detection: Advancing Environmental Monitoring Efficiency\u201d PLOS ONE 20(5): e0322448. DOI: 10.1371\/journal.pone.0322448.<\/li>\n
  14. [14] Y. Chen et al., (2025) \u201cGasEdge-YOLO: Multi-Scale Edge Generator and Star-Shaped Branch Attention for Efficient Infrared Gas Detection\u201d Measurement Science and Technology 36(10): 106012. DOI: 10.1088\/1361-6501\/ae1319.<\/li>\n
  15. [15] H. S. Mahmood et al., (2024) \u201cConducting In-Depth Analysis of AI, IoT, Web Technology, Cloud Computing, and Enterprise Systems Integration for Enhancing Data Security and Governance to Promote Sustainable Business Practices\u201d Journal of Information Technology and Informatics 3(2): 297\u2013332. URL: https:\/\/www.researchgate.net\/profile\/Dildar<\/a>.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

    <\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,1682,6],"tags":[1724],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":" Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Download Citation:\u00a0 BibTeX | http:\/\/dx.doi.org\/10.6180\/jase.202611_34.042\u00a0\u00a0 Download PDF To address limitations in real-time responsiveness, spatial perception, and long-term stability…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/9854"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9854"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9854"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9854"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}